AMERICAN
METEOROLOGICAL
SOCIETY
Bulletin of the American Meteorological Society
EARLY ONLINE RELEASE
This is a preliminary PDF of the author-produced
manuscript that has been peer-reviewed and
accepted for publication. Since it is being posted
so soon after acceptance, it has not yet been
copyedited, formatted, or processed by AMS
Publications. This preliminary version of the
manuscript may be downloaded, distributed, and
cited, but please be aware that there will be
visual
differences
and possibly some content differences
between this version and the final published version.
The DOI for this manuscript is doi:
10.1175/BAMS-D-13-00108.1
The final published version of this manuscript will replace
the preliminary version at the above DOI once it is
available.
©
2013 American Meteorological SocietyMeteorology For Coastal/Offshore Wind Energy In The United States:
1
Recommendations And Research Needs For The Next 10 Years
2
Cristina L. Archer (corresponding author) 3
University of Delaware 4
College of Earth, Ocean, and Environment
5 Newark, DE 19716 6 carcher@udel.edu 7 8 Brian A. Colle 9
Stony Brook University/SUNY, Stony Brook, New York 10
11
Luca Delle Monache 12
National Center for Atmospheric Research, Boulder, Colorado 13
14
Michael J. Dvorak 15
Sailor’s Energy, Berkeley, California 16
17
Julie Lundquist 18
University of Colorado at Boulder, and 19
National Renewable Energy Laboratory, Golden, Colorado 20
21
Bruce H. Bailey and Philippe Beaucage 22
AWS Truepower, LLC, Albany, New York 23
24
Matthew J. Churchfield 25
National Renewable Energy Laboratory, Golden, Colorado 26
27
Anna C. Fitch and Branko Kosovic 28
National Center for Atmospheric Research, Boulder, Colorado 29
30
Sang Lee and Patrick J. Moriarty 31
National Renewable Energy Laboratory, Golden, Colorado 32
33
Hugo Simao 34
Princeton University, Princeton, New Jersey 35
36
Richard J. A. M. Stevens 37
Johns Hopkins University, Baltimore, Maryland, and 38
University of Twente, Enschede (The Netherlands) 39
40
Dana Veron 41
University of Delaware, Newark, Delaware 42
43
John Zack 44
AWS Truepower, LLC, Albany, New York 45
Offshore wind energy is just starting in the United States, with imminent offshore wind 46
farms in Massachusetts, Maryland, and Rhode Island waters and with an ambitious goal 47
of 10 GW of installed offshore capacity by 2020 set by the U.S. Department of Energy 48
(DOE), which has recently funded seven “Advanced Technology Demonstration” 49
offshore wind projects to help achieve that goal. Although new in the U.S., offshore wind 50
energy began over 20 years ago in Europe and has now reached over 5.5 GW of installed 51
capacity worldwide, predominantly in Denmark and the United Kingdom. Given the 52
unfortunate coincidence of introducing a new industry during challenging economic 53
times, it is essential that public and private financial resources be effectively and 54
optimally directed towards those meteorological research needs that are emerging today 55
and that will be critical in the next decade. Identifying these research needs for wind 56
energy along the U.S. East Coast, both coastal and offshore, was the goal of a two-day 57
symposium held at the University of Delaware on 2728 February 2013. Over 40 58
participants gathered from academia, national laboratories, wind industry, and funding 59
agencies. 60
61
During the symposium, three main topics were explored: 1) wind resource assessment, 2) 62
wind power forecasting, and 3) turbulent wake losses. Overviews of the latest findings in 63
the three topics were given on the first day in the form of presentations, which were open 64
to students and the general public. On the second day, the experts gathered in a workshop 65
to identify research needs and provide recommendations for urgent action items. Whereas 66
specific research needs were identified for each of the three main topics, two emerged as 67
cross-cutting and urgent: 1) continuous, publicly available, multilevel measurements of 68
winds and temperature over U.S. offshore waters, and 2) quantification and reduction of 69
uncertainty. These two research needs and relevant recommendations (in italics) are 70
described first. 71
72
Research need #1: More offshore observations
73 74
Offshore meteorological measurements are challenging and expensive. Ideal 75
measurements would quantify the wind resource at several vertical levels spanning the 76
height of the turbine rotor disk to understand the rotor equivalent wind speed and possible 77
impacts on turbine power production. In European waters, designated research platforms 78
(e.g., FINO1 in Germany) have been established for characterization of offshore flow as 79
well as validation of new measurement technologies such as light detection and ranging 80
(lidar) and modeling approaches. The few long-term meteorological observations off the 81
East Coast are typically buoy-based, thereby restricting the altitude of wind 82
measurements to a few meters above the surface. A sparse network of nine towers, with 83
an elevation of ≤50 m, extends along the coast from Florida to Maine, but fails to provide 84
multilevel information and measurements at turbine hub-height or above. 85
Periodically, detailed measurements of wind and temperature have been conducted 86
offshore in short-term field campaigns, but the consistent long-term measurements 87
required for resource assessment are generally not available off the East Coast (with the 88
only exception being the Cape Wind tower in Nantucket Sound, Massachusetts). The 89
standard approach considered buoy measurements and then extrapolated them to higher 90
altitudes with assumptions of the shape of the wind profile (log-law or power-law). By 91
extrapolating surface or near-surface measurements with such smooth profiles, important 92
wind structures such as low-level jets are ignored. 93
The first recommendation is the deployment of a more dense network of
94
meteorological towers, which will enable traditional resource assessment measurements
95
such as wind speed, wind direction, and turbulence at several levels from the surface to 96
the rotor disk top, and temperature profiles for quantifying atmospheric stratification and 97
stability. Ideally, such towers could also provide a platform for validating remote sensing 98
measurements. The U.S. DOE has proposed the Reference Facility for Offshore 99
Renewable Energy (RFORE) to be located at the Chesapeake Light Tower, 100
approximately 13 miles off the Virginia Coast. The facility provides a first step towards 101
addressing the shortage of offshore wind data. 102
Beyond meteorological towers, remote sensing technology mounted either on fixed 103
towers or on floating platforms could provide data over broader regions. Scanning
104
Doppler lidar, wind-profiling lidar, and sodar can provide valuable wind speed and 105
direction measurements throughout the turbine rotor disk and beyond. Radiometers can 106
quantify temperature and humidity profiles to determine atmospheric stability. 107
In addition to long-term measurements of winds, temperature, and moisture profiles, 108
short-term intensive measurement campaigns with a broader deployment of instruments
109
would also be of value, especially for model validation.
110
These recommendations for more intensive observations extend a prior call for more 111
onshore meteorological observations and focused field campaigns made by DOE in 2008. 112
Since then, new types of remote sensing instruments have become more widely available 113
and more accepted in the wind energy industry for wind resource characterization. 114
115
Research need #2: Uncertainty characterization
116 117
Deterministic wind power forecasts based on numerical weather prediction (NWP) can 118
provide useful information for decision-making. However, by design, a single plausible 119
future state of the atmosphere starting from a single initial state is generated. Imperfect 120
initial and boundary conditions and model deficiencies inevitably lead to nonlinear error 121
growth during model integration. Accurate knowledge of the continuum of plausible 122
future states, the forecast probability density function (PDF), is considerably more useful 123
for decision-making because it allows for a quantification of the uncertainty associated 124
with a forecast. 125
“Ensembles” are used today to generate a set of plausible future atmospheric states 126
and to estimate the forecast PDF of atmospheric variables relevant to wind power. 127
Ensembles are created from the outputs of NWP models using any of the following: 128
various initial conditions, different parameterizations within a single model, stochastic 129
approaches with diverse numerical schemes, different models, and coupled ocean-130
atmosphere schemes. For wind energy, one important additional source of uncertainty 131
comes from the challenging step of wind-to-power conversion. 132
Ensembles are affected by biases in the ensemble mean and by lack of diversity 133
among the ensemble members, particularly in the planetary boundary layer (PBL). 134
Therefore, post-processing is an important component of the wind forecasting process 135
and should be explored further, preferably including methods and techniques developed
136
by the wind industry. Since the wind industry benefits from the findings published by the 137
research community and the public sector, it is recommended that a regular two-way 138
exchange of know-how between academia, public sector, and industry be established to
139
help advance the science and prevent the duplication of efforts. A promising post-140
processing technique is the analog approach, in which past observations that correspond 141
to past predictions that best match selected features of the current forecast, such as time 142
series of wind speed and direction, are used to correct the current forecast. Other 143
promising techniques are advanced model output statistics (e.g., neural networks, support 144
vector machines, and random forests). 145
Recently, operational centers have generated multiyear reforecast datasets to support 146
successful calibration of both deterministic and probabilistic forecasts. It is expected that 147
in the next few years new calibration techniques, possibly combining statistical and 148
dynamical approaches, will lead to large improvements in the accuracy of wind power 149
predictions and in the reliable characterization of their uncertainty. 150
151
Next, the three main topics and their specific research needs are described. 152
153
Topic #1: Resource assessment
154 155
Initial maps of the U.S. offshore wind resource from the National Renewable Energy 156
Laboratory (NREL) and others by Stanford University have identified gross 157
characteristics of the hub-height offshore wind resource, which have been generally 158
useful to policy makers and researchers and for early-stage project development. Using 159
mesoscale modeling techniques, these maps provide estimates of wind speed and 160
direction, diurnal and seasonal patterns, wind shear, and air density at horizontal grid 161
scales of approximately 1-5 km. This information, although essentially unverified due to 162
the lack of hub-height measurements described in Research Need #1, has enabled 163
numerous project siting studies, wind farm layout and energy production simulations, and 164
estimates of development potential as a factor of water depth, distance from shore, wind 165
resource, and other factors. However, there is a need to accurately capture dynamic 166
coastal processes, such as sea breezes, low-level jets, and other land-air-ocean
167
interactions, as they represent a significant source of variability in the available wind. 168
Data representing assessment periods of 2025 years (i.e., project lifetimes) are 169
typically required for bankable offshore projects; interannual speed variability of 4%6% 170
is not uncommon. The probability and magnitude of extreme events, particularly peak 171
winds and waves and hurricanes, and the effects of more common events, such as winter 172
storms, icing from sea spray, and salt corrosion, need to be better known to properly 173
design turbines and foundations and meet industry standards. In a changing climate, more 174
studies are needed to reduce the uncertainty of a changing wind resource as ocean,
175
offshore, and coastal temperatures change. Changes in the local wind environment over 176
time may also be caused by the increasing presence of other wind farms within a given 177
region, as described in Topic #3. 178
Recent studies have explored strategic temporal, climatological, and spatial aspects of 179
the offshore resource, including large-scale wind farm interconnection scenarios.U.S. 180
East Coast offshore wind has been found to be particularly coincident with peak-181
electricity demand. Similar studies should be performed to identify resource attributes 182
that can add value to generally higher offshore costs and evaluate the sensitivity of 183
project location, including distance from the shore, to load coincidence. 184
Significant offshore resource assessment uncertainties exist. Most of the 185
aforementioned studies relied on mesoscale modeling that was validated with generally 186
sparse in-situ data. Perhaps the largest uncertainty is extrapolating surface observations, 187
generally 5-m buoy anemometer measurements to heights across the turbine rotor. As 188
such, there is an urgent need for multilevel wind and temperature observations at 189
platforms offshore (as in Research Need #1), equipped with either meteorological towers
190
that are as tall or taller than hub height, or lidars. In the coastal region, transport 191
processes (advection of either maritime air inland or continental air offshore) during sea 192
and land breeze events often cause the PBL to deviate from classic well-mixed, neutrally-193
stable conditions. Existing PBL parameterizations struggle to perform well in these 194
conditions. Research effort is needed to improve such PBL parameterizations in coastal 195
regions.
196
Long-term wind climatologies require publicly available historic reanalysis data and 197
future climate data generated by models forced under different anthropogenic emission 198
scenarios. Most of the existing publicly available data are at a relatively coarse spatial 199
scale (>20 km) compared to the size of a typical wind farm. Dynamical downscaling 200
methods typically employ a regional climate model to generate higher spatio-temporal 201
wind climatologies but at a high computational expense for long climate records. 202
Stochastic downscaling methods are computationally cheaper and have been shown to 203
accurately downscale low-resolution reanalysis data with acceptable accuracy, as 204
compared to in-situ validation data. 205
206
Topic #2: Wind Power Forecasting
207 208
Wind power forecasting is challenging because the relationship between wind speed and 209
power production for a single wind turbine or a wind farm is nonlinear; for some wind 210
speed ranges, the sensitivity of power production forecasts to wind speed forecast error is 211
quite high. For example, a modest 1.5 m s-1 error in a wind speed forecast can, in some 212
cases, result in a power production forecast error of over 20% of a wind farm’s capacity. 213
A diverse set of prediction tools and input data have been applied to the wind power 214
forecast problem for a range of time scales. Intra-hour forecasts (060 minutes ahead) are 215
needed for regulation and real-time dispatch decisions. At this scale, the effects of small 216
eddies and turbulent mixing are important but cannot be resolved by operational models. 217
Therefore, mainly statistical methods are used, which are based on near real-time 218
observations. This has driven the deployment of meteorological sensors and lidars for 219
intra-hour forecasting. 220
The 1-6 hour-ahead forecast for load-following and next-operating hour commitment 221
has to account for various mesoscale weather phenomena (e.g., sea breezes, convective 222
systems, and local topography). The rapid-update NWP approach most likely offers the 223
best potential for improvement in this time frame. This is a tool with increasing 224
capability, largely because of improvements in data assimilation techniques (e.g., the 225
hybrid ensemble Kalman filter approach), the formulations of physics-based submodels, 226
and the amount and quality of data available for assimilation. The state of the art in rapid-227
update systems is the High-Resolution Rapid Refresh (HRRR) model, currently 228
undergoing experimental operation at the National Oceanic and Atmospheric 229
Administration, which assimilates the latest data and generates a 15-hour forecast on a 3-230
km grid every hour. 231
The day-ahead forecast is important for unit commitment, scheduling, and market 232
trading, which require knowledge of the evolving synoptic storm systems using NWP 233
models and ensembles. The seasonal predictions for resource planning and contingency 234
analysis require knowledge of global teleconnections (such as El Niño). These 235
predictions are based largely on the analysis of cyclical patterns and climate forecast 236
system models. 237
It is also recommended that more offshore observations be collected using towers,
238
lidars, and buoys, to better validate models, help with data assimilation and uncertainty
239
characterization, and improve the model physics, because many of the PBL schemes were 240
originally developed over land. These efforts will require a close collaboration between 241
operational forecast centers, industry, and academia. 242
Lastly, future efforts should focus on improving the models’ ability to represent the 243
PBL and the interactions of fine-scale processes with larger scale flows, both inland and
244
offshore. Such improvements will be possible only with investments that focus on 245
improving our understanding of these key processes using real observations. Several 246
workshops over the past twenty years have noted the need for improved PBL modeling, 247
but no concerted effort at making such improvements has been made. 248
249
Topic #3: Turbulent Wake Losses
250 251
Wind turbines generate wakes downstream, which are generally characterized by a wind 252
speed deficit and higher turbulence than the upwind environment. Because wakes can 253
reduce power production and increase structural fatigue in downstream turbines, 254
understanding wake properties, quantifying resulting power losses, and optimizing wind 255
turbine layouts to minimize such losses is especially important to the wind energy 256
industry. Accurately modeling turbine wakes is also important for other atmospheric 257
applications that span a wide range of spatial scales, such as the impacts of wind energy 258
deployment on the global climate, local meteorology, crop production, and the wind 259
resource itself. 260
Because atmospheric flows are characterized by high Reynolds numbers (~107-108), 261
the number of grid points required to explicitly resolve such flows with operating wind 262
turbines via direct numerical simulation is ~1018, which is prohibitive in the foreseeable 263
future. As such, the wind industry has traditionally relied on computationally efficient 264
wake models to simulate wind turbine wakes. In order of increasing complexities, these 265
earlier wake models include: analytical representations of the wake deficit (e.g., the 266
PARK model); parabolized forms of the Reynolds-Averaged Navier-Stokes (RANS) 267
equations (e.g., the Ainslie model, also called the eddy viscosity model; UPMPARK, 268
which uses a k-ɛ turbulence closure); hybrid models based on an internal boundary layer 269
growth parameterization and coupled with a parabolized RANS or an analytical model 270
(e.g., Deep-Array Wake Model and Large Array Wind Farm model); and nonlinear 271
RANS models (e.g., WindModeller, Ellipsys, and FUGA). Although these models are 272
attractive for their quick runtime, they have limited ability to capture the detailed wake 273
characteristics because they are not suitable for simulations of unsteady, anisotropic 274
turbulent flows. 275
To overcome these limitations, the research community has been using large-eddy 276
simulation (LES), in which large-scale flow structures are resolved while the effects of 277
smaller eddies are represented with a subgrid model (Smagorinsky or dynamical). In 278
addition, the wind turbine is represented by either an actuator disk (with or without 279
rotation features) or by actuator lines (one per blade) that exert a force on the flow and 280
act as a momentum sink, or by the vortex method. Arrays of multiple wind turbines, in 281
which multiple wakes interact with one another, have also been successfully simulated 282
with LES. However, because of the high CPU-hours required, LES can be conducted for 283
only a few hours or at equilibrium-state using periodic boundary conditions. 284
Because LES models for turbine wakes were traditionally developed in-house by 285
research centers or universities without any funds for distributing, maintaining, or testing 286
the codes, they are generally not available to the public. The only exception is the open-287
source Simulator for Offshore/Onshore Wind Farm Applications (SOWFA) from NREL, 288
which includes a finite-volume scheme, actuator disks/lines, and options for periodic or 289
nonperiodic boundary conditions. Although developing numerous in-house LES codes is 290
of value because researchers can obtain independent verification of results, it is 291
recommended that more effort and funds be devoted to maintaining LES codes for turbine
292
wakes and making them available to the public.
293
To avoid the steep computational costs of simulating real wind farms with high 294
numbers of turbines via LES, parameterizations of the effects of large wind farms on 295
regional meteorology and global climate have been developed for mesoscale NWP and 296
large-scale climate models, which are less computationally demanding. These 297
parameterizations represent wind farms as either an elevated momentum sink (often with 298
an added source of turbulent kinetic energy (TKE), increased surface roughness, or an 299
increased surface drag coefficient. Because surface-based parameterizations incorrectly 300
extract momentum near the surface, as opposed to around hub height, they are not
301
recommended for turbine wake impact studies. Although the global-scale impacts of even
302
high penetrations of wind energy have been proven negligible, local wakes extending 303
tens of kilometers downwind of individual large wind farms have been generated by 304
some wind farm parameterizations. However, to date, few observations are available to 305
verify these model results. 306
Comparing model results with wind tunnel experiments, with either a single turbine or
307
multiple turbines, is useful because the constant and controllable environment in a wind
tunnel can be reproduced well. However, wind tunnel conditions are different from real 309
atmospheric conditions and therefore field measurements are also recommended both at 310
individual turbines and at offshore wind farms. Short-term field campaigns, as well as
311
routine measurements (especially offshore) are needed to validate results under a large 312
umbrella of atmospheric conditions. It is recommended that inflow, near-wake, and far-313
wake vertical wind profiles and atmospheric stability be measured, as well as wake
314
properties, such as TKE and turbulent fluxes (preferably with scanning lidars or arrays of
315
sonic anemometers). 316
317
More information on offshore observations (Research need #1)
318 319
Cañadillas, B., A. Westerhellweg, and T. Neumann, 2011: Testing the performance of a 320
ground-based wind LiDAR system: One year intercomparison at the offshore platform 321
FINO1, DEWI Mag., 38, 58-64. 322
Chen, S. S., W. Zhao, M. A. Donelan, J. F. Price, and E. J. Walsh, 2007: The CBLAST-323
Hurricane Program and the Next-Generation Fully Coupled Atmosphere-Wave-Ocean 324
Models for Hurricane Research and Prediction. Bull. Amer. Meteor. Soc., 88, 311–317. 325
doi: http://dx.doi.org/10.1175/BAMS-88-3-311. 326
Clifton, A., S. Schreck, D. Jager, N. Kelley, and J. K. Lundquist, 2013: Meteorological 327
tower observations at the National Renewable Energy Laboratory. Journal of Solar 328
Energy Engineering. 135(3), 031017, doi:10.1115/1.4024068
329
Edson, J., and coauthors, 2007: The Coupled Boundary Layers and Air-Sea Transfer 330
Experiment in Low Winds. Bull. Amer. Meteor. Soc., 88, 341–356. doi: 331
http://dx.doi.org/10.1175/BAMS-88-3-341. 332
Friedrich, K. J., K. Lundquist, E. Kalina, M. Aitken, and R. Marshall, 2012: Stability and 333
Turbulence in the Atmospheric Boundary Layer: An Intercomparison of Remote 334
Sensing and Tower Observations. Geophys. Res. Lett., Vol. 39, No. 3, L03801, 335
doi:10.1029/2011GL050413. 336
Knupp, K., R. Ware, D. Cimini, F. Vandenberghe, J. Vivekanandan, E. Westwater, and 337
T. Coleman, 2009: Ground-Based Passive Microwave Profiling during Dynamic 338
Weather Conditions. J. Atmos. Oceanic Technol., 26, 1057-1073. 339
Muñoz-Esparza, D., B. Cañadillas, T. Neumann, and J. van Beeck, 2012: Turbulent 340
fluxes, stability and shear in the offshore environment: Mesoscale modelling and field 341
observations at FINO1. J. Renewable Sustainable Energy, 4, 063136, doi: 342
10.1063/1.4769201. 343
Neumann, T., K. Nolopp, and K. Herklotz, 2004: First operating experience with the 344
FINO1 research platform in the North Sea, DEWI Mag., 24, 27-34. 345
Nunalee, C. G., S. and Basu, 2013: Mesoscale modeling of coastal low-level jets: 346
implications for offshore wind resource estimation. Wind Energy, 347
doi: 10.1002/we.1628 348
Pichugina, Y. L., R. M. Banta, W. A. Brewer, S. P. Sandberg, and R. M. Hardesty, 2012: 349
Doppler Lidar-Based Wind-Profile Measurement System for Offshore Wind-Energy 350
and Other Marine Boundary Layer Applications. J. Appl. Meteor. Climatol., 51, 327– 351
349. 352
Schreck, S., J. K. Lundquist, W. Shaw, 2008: DOE Workshop Report: Research Needs 353
for Wind Resource Characterization. NREL Technical Report NREL/TP-500-43521. 354
Shaw, W. J., J. K. Lundquist, S. Schreck, 2009: Workshop on Research Needs for Wind 355
Resource Characterization. Bull. Amer. Meteorol. Soc., 90, 535-538, doi: 356
10.1175/2008BAMS2729.1 357
Wagner, R., I. Antoniou, S. M. Pedersen, M. S. Courtney, and H. E. Jørgensen, 2009: 358
The influence of the wind speed profile on wind turbine performance measurements. 359
Wind Energy, 12, 348–62.
360
Wharton, S., and J. K. Lundquist, 2012: Atmospheric stability affects wind turbine power 361
collection. Environ. Res. Lett., 7, 014005, doi:10.1088/1748-9326/7/1/014005. 362
363
More information on uncertainty characterization (Research need #2) and wind
364
power forecasting (Topic #2)
365 366
AMS, 2008: Enhancing weather information with probability forecasts: An information
367
statement of the American Meteorological Society. Bull. Amer. Meteor. Soc., 89,
368
1049–1053.
369
ANL, 2009: Wind power forecasting: State-of-the-art 2009. Decision and Information 370
Sciences Division Document ANL-DIS-10-1, 216 pp., available online at 371
www.dis.anl.gov/pubs/65613.pdf.
372
Berner, J., G. J. Shutts, M. Leutbecher, and T. N. Palmer, 2009: A spectral stochastic 373
kinetic energy backscatter scheme and its impact on flow-dependent predictability in 374
the ECMWF ensemble prediction system. J. Atmos. Sci., 66, 603–626. 375
Bishop, C. H., and Z. Toth, 1999: Ensemble transformation and adaptive observations. J. 376
Atmos. Sci., 56, 1748–1765.
Bowler, N. E., A. Arribas, K. R. Mylne, K. B. Robertson, and S. E. Beare, 2008: The
378
MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134,
379
703–722.
380
Buizza, R., M. J. Miller, and T. N. Palmer, 1999: Stochastic simulation of model 381
uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 382
125, 2887–2908.
383
Clifton, A., L. Kilcher, J. K. Lundquist, and P. Fleming, 2013: Using machine-learning to 384
predict wind turbine power output. Env. Res. Lett. 8 024009 doi:10.1088/1748-385
9326/8/2/024009. 386
Eckel, F. A., and C. F. Mass, 2005: Aspects of effective mesoscale, short-range ensemble 387
forecasting. Wea. Forecasting, 20, 328–350. 388
Erickson, M. E., B. A. Colle, and J. Charney, 2012: Impact of bias correction type and 389
conditional training on Bayesian model averaging over the northeast United States. 390
Wea. Forecasting, 27, 1449-1469.
391
European Wind Energy Association, 2013: The European offshore wind industry - Key 392
trends and statistics. European Wind Energy Association, Brussels, Belgium, 31 pp. 393
Hacker, J., S.-Y. Ha, C. Snyder, J. Berner, F. Eckel, E. Kuchera, M. Pocernich, S. Rugg, 394
J. Schramm, and X. Wang, 2011: The U.S. Air Force Weather Agency’s mesoscale 395
ensemble: Scientific description and performance results. Tellus A, 63, 625–641. 396
Hagedorn, R., T. M. Hamill, and J. S. Whitaker, 2008: Probabilistic forecast calibration
397
using ECMWF and GFS ensemble reforecasts. Part I: two-meter temperatures. Mon.
398
Wea. Rev., 136, 2608–2619.
Hamill, T. M., J. S. Whitaker, and X. Wei, 2004: Ensemble reforecasting: improving
400
medium-range forecast skill using retrospective forecasts. Mon. Wea. Rev., 132, 1434–
401
1447.
402
Hamill, T. M., and J. S. Whitaker, 2006: Probabilistic quantitative forecasts based on 403
reforecast analogs: theory and application. Mon. Wea. Rev., 134, 3209–3229.
404
Hamill, T. M., J. S. Whitaker, and S. L. Mullen, 2006: Reforecasting: improving skill 405
using retrospective forecasts. Bull. Amer. Meteor. Soc., 27, 33–46. 406
Hamill, T. M., and J. S. Whitaker, 2007: Ensemble calibration of 500-hPa geopotential
407
height and 850-hPa and 2-m temperatures using reforecasts. Mon. Wea. Rev., 135,
408
3273–3280.
409
Hamill, T. M., R. Hagedorn, and J. S. Whitaker, 2008: Probabilistic forecast calibration
410
using ECMWF and GFS ensemble forecasts. Part II: Precipitation. Mon. Wea. Rev.,
411
136, 2620–2632.
412
Hamill, T. M., G. T. Bates, J. S. Whitaker, D. R. Murray, M. Fiorino, T. J. Galarneau, Jr., 413
Y. Zhu, and W. Lapenta, 2012: NOAA’s second-generation global medium-range 414
ensemble 415
Hirschberg, P. A., E. Abrams, A. Bleistein, W. Bua, L. Delle Monache, T. W. Dulong, J.
416
E. Gaynor, B. Glahn, T. M. Hamill, J. A. Hansen, D. C. Hilderbrand, R. N. Hoffman, 417
B. H. Morrow, B. Philips, J. Sokich, and N. Stuart, 2011: A weather and climate 418
enterprise strategic implementation plan for generating and communicating forecast 419
uncertainty information. Bull. Amer. Meteor. Soc., 12, 1651–1666.
420
Holt, T., J. Pullen, and C. H. Bishop, 2009: Urban and ocean ensembles for improved 421
meteorological and dispersion modelling of the coastal zone. Tellus A, 61, 232–249. 422
Hou, D., E. Kalnay, and K. K. Droegemeier, 2001: Objective verification of the
423
SAMEX’98 ensemble forecasts. Mon. Wea. Rev., 129, 73–91.
424
Houtekamer, P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, 1996: A 425
system simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 1225–1242. 426
Houtekamer, P. L., H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and 427
B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: 428
Results with real observations. Mon. Wea. Rev., 133, 604–620. 429
Houtekamer, P. L., H. L. Mitchel, and X. Deng, 2009: Model error representation in an 430
operational Ensemble Kalman Filter. Mon. Wea. Rev., 137, 2126-2143. 431
Jones, M., B. A. Colle, and J. Tongue, 2007: Evaluation of a short-range ensemble 432
forecast system over the Northeast U.S., Wea. Forecasting, 22, 36-55. 433
Krishnamurti, T. N., C. M. Kishtawal, Z. Zhang, T Larow, D. Bachiochi, E. Williford, S. 434
Gadgil, and S. Surendran, 2000: Multimodel ensemble forecasts for weather and
435
seasonal climate. Journal of Climate, 13, 4196–4216.
436
Kuhl, D., I. Szunyogh, E. J. Kostelich, G. Gyarmati, D. J. Patil, M. Oczkowski, B. R. 437
Hunt, E. Kalnay, E. Ott, and J. A. Yorke, 2007: Assessing predictability with a local 438
ensemble Kalman filter. J. Atmos. Sci., 64, 1116–1140. 439
Lorenz, E. N., 1963: Deterministic non-periodic flow. J. Atmos. Sci., 20, 130–141.
440
Marquis, M., J. Wilczak, M. Ahlstrom, J. Sharp, A. Stern, J. C. Smith, and S. Calvert, 441
2011: Forecasting the wind to reach significant penetration levels of wind energy. 442
Bull. Amer. Meteor. Soc., 92, 1159–1171. 443
Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The new ECMWF
444
ensemble prediction system: Methodology and validation. Quart. J. Roy. Meteor. Soc.,
445
122, 73–119.
NRC, 2006: Completing the Forecast: Characterizing and Communicating Uncertainty
447
for Better Decisions Using Weather and Climate Forecasts. National Academies
448
Press, 124 pp.
449
NREL, 2011: Wind energy forecasting: A collaboration of the National Center for 450
Atmospheric Research (NCAR) and Xcel Energy, available online at 451
http://www.nrel.gov/docs/fy12osti/52233.pdf. 452
Perlin, N., E.D. Skyllingstad, R.M. Samelson, and P.L. Barbour, 2007: Numerical 453
simulation of air–sea coupling during coastal upwelling. Journal of Physical 454
Oceanography, 37, 2081–2093.
455
Stensrud, D. J., J. W. Bao, and T. T. Warner, 2000: Using initial condition and model 456
physics perturbations in short-range ensemble simulations of mesoscale convective 457
systems. Mon. Wea. Rev., 128, 2077–2107. 458
Teixeira, J., and C. A. Reynolds, 2008: Stochastic nature of physical parameterizations in 459
ensemble prediction: A stochastic convection approach. Mon. Wea. Rev., 136, 483– 460
496. 461
Thomas, S. J., J. P. Hacker, M. Desgagné, and R. Stull, 2002: An ensemble analysis of
462
forecast errors related to floating point performance. Wea. Forecasting, 17, 898–906.
463
Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of
464
perturbations. Bull. Am. Meteorol. Soc., 74, 2317–2330.
465
Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP: The breeding method.
466
Mon. Wea. Rev., 125, 3297–3319.
467
Wandishin, M. S., S. L. Mullen, D. J. Stensrud, and H. E. Brooks, 2001: Evaluation of a
468
short-range multi-model ensemble system. Mon. Wea. Rev., 129, 729–747.
WMO, 2008: Guidelines on communicating forecast uncertainty. WMO Tech. Document
470
4122, 25 pp, available online at www.wmo.int/pages/prog/amp
/pwsp/documents/TD-471
1422.pdf.
472
Wilks, D. S., and T. M. Hamill, 2007: Comparison of ensemble-MOS methods using
473
GFS reforecasts. Mon. Wea. Rev., 135, 2379–2390.
474
Wilks, D. S., 2009: Extended logistic regression to provide full-probability-distribution
475
MOS forecasts. Meteorol. Appl., 16, 361–368.
476 477
More information on resource assessment (Topic #1)
478 479
Brower, M., 2012: Wind resource assessment: a practical guide to developing a wind 480
project. John Wiley & Sons, Hoboken (New Jersey), 280 pp. 481
Delle Monache, L., T. Nipen, Y. Liu, G. Roux, and R. Stull, 2011: Kalman filter and 482
analog schemes to post-process numerical weather predictions. Mon. Wea. Rev., 139, 483
3554-3570. 484
Dvorak, M. J., E. D. Stoutenburg, C. L. Archer, W. Kempton, and M. Z. Jacobson, 2012: 485
Where is the ideal location for a US East Coast offshore grid? Geophysical Research 486
Letters, 39, L06804, 6 pp., doi:10.1029/2011GL050659.
487
Dvorak, M. J., B. A. Corcoran, J. E. Ten Hoeve, N. G. McIntyre, M. Z. Jacobson, 2013: 488
US East Coast offshore wind energy resources and their relationship to peak-time 489
electricity demand. Wind Energy, in press. 490
Kempton, W., C. L. Archer, A. Dhanju, R. W. Garvine, and M. Z. Jacobson, 2007: Large 491
CO2 reductions via offshore wind power matched to inherent storage in energy
end-492
uses. Geophysical Research Letters, 34, L02817, doi:10.1029/2006GL028016. 493
Kempton, W., F. Pimenta, D. E. Veron, and B. A. Colle, 2010: Electric power from 494
offshore wind via synoptic-scale interconnection. Proc. Natl. Acad. Sci., 107,7240. 495
496
More information on wake characteristics and losses (Topic #3)
497 498
Adams, A. S., and D. W. Keith, 2013: Are global wind power resource estimates 499
overstated? Environ. Res. Lett. 8, 015021. 500
Ainslie, J. F., 1988: Calculating the flowfield in the wake of wind turbines. J. 501
Wind Eng. Ind. Aerod., 27, 213-224.
502
American Meteorological Society Offshore Wind Energy Committee, 2013: The 503
need for expanded meteorological and oceanographic data to support resource 504
characterization and design condition definition for offshore wind power 505
projects in the United States. Available at: 506
http://www.ametsoc.org/boardpges/cwce/docs/BEP/Offshore-Wind/2013-05-507
Offshore-Wind-APT-Final-Report.pdf 508
Baidya Roy, S., S. W. Pacala, and R. L. Walko, 2004: Can large wind farms affect 509
local meteorology? J. Geophys. Res., 109, D19101. 510
Baidya Roy S, 2011: Simulating impacts of wind farms on local 511
hydrometeorology. J. Wind Eng. Ind. Aerodyn. doi:10.1016. 512
Barrie D., and D. B. Kirk-Davidoff, 2010: Weather response to a large wind 513
turbine array. Atmos. Chem. Phys., 10, 769-775. 514
Barthelmie, R. J., G. C. Larsen, S. T. Frandsen, L. Folkerts, K. Rados, S. C. Pryor, 515
B. Lange, G. Schepers, 2006: Comparison of Wake Model Simulations with 516
Offshore Wind Turbine Wake Profiles Measured by Sodar. J. Atmos. Oceanic 517
Technol., 23, 888–901.
518
Barthelmie, R. J., K. Hansen, S.T. Frandsen, O. Rathmann, J. G. Schepers, W. 519
Schlez, J. Phillips, K. Rados, A. Zervos, E.S. Politis, and P. Chaviaropoulos, 520
2009: Modelling and Measuring Flow and Wind Turbine Wakes in Large 521
Wind Farms Offshore, Wind Energy 12, 431. 522
Beaucage, P., N. Robinson, M. Brower, and C. Alonge, 2012: Overview of six 523
commercial and research wake models for large offshore wind farms. 524
Proceeding, EWEA conference, Copenhagen. 525
Cal, R. B., J. Lebrón, L. Castillo, H. S. Kang, and C. Meneveau, 2010: 526
Experimental study of the horizontally averaged flow structure in a model 527
wind-turbine array boundary layer. J. Renewable Sustainable Energy, 2, 528
013106. 529
Calaf, M., C. Meneveau, and J. Meyers, 2010: Large eddy simulation study of 530
fully developed wind-turbine array boundary layers. Phys. Fluids, 22, 015110. 531
Calaf, M., M. B. Parlange, and C. Meneveau, 2011: Large eddy simulation study 532
of scalar transport in fully developed wind-turbine array boundary layers. Phys. 533
Fluids, 23, 126603.
534
Chamorro, L.P., and F. ort -Agel, 2009: A tunnel investigation of wind-535
turbine wakes: boundary-layer turbulence effects. Bound.-Lay. Meteor., 132, 536
129–149. 537
Churchfield, M. J., S. Lee, S., J. Michalakes, and P. Moriarty, 2012: A numerical 538
study of the effects of atmospheric and wake turbulence on wind turbine 539
dynamics. J. Turbulence, 13(14), 1-32. 540
Churchfield, M. J., S. Lee, P. Moriarty, L. A. Martinez, S. Leonardi, G. 541
Vijayakumar, and J. G. Brasseur, 2012: A large-eddy simulation of wind-plant 542
aerodynamics, 50th AIAA Aerospace Sciences Meeting including the New 543
Horizons Forum and Aerospace Exposition, Nashville, Tennessee, 912 544
January 2012. 545
Crespo, A., L. Chacón, J. Hernández, F. Manuel, and J. Grau, 1994: UPMPARK: 546
A Parabolic 3D Code to Model Wind Farms, Proceeding, European Wind 547
Energy Conference 1994, Thessalonica, Greece, 10-14 October 1994,
454-548
459. 549
Fitch, A. C., J. B. Olson, J. K. Lundquist, J. Dudhia, A. K. Gupta, J. Michalakes, 550
and I. Barstad, 2012: Local and mesoscale impacts of wind farms as 551
parameterized in a mesoscale NWP model. Mon. Weather Rev., 140, 3017-552
3038. 553
Fitch, A. C., J. B. Olson, and J. K. Lundquist, 2013: Mesoscale influences of wind 554
farms throughout a diurnal cycle. Mon. Weather Rev., 141, 2173-2198. 555
http://dx.doi.org/10.1175/MWR-D-12-00185.1. 556
Fitch, A. C., J. B. Olson, and J. K. Lundquist, 2013: Parameterization of wind 557
farms in climate models. J. Climate. http://dx.doi.org/10.1175/JCLI-D-12-558
00376.1. 559
Ivanell, S. S. A., 2009: Numerical computations of wind turbine wakes, PhD 560
Thesis, Gotland University, Stockholm, Sweden. 561
Ivanell, S. S. A., R. Mikklesen, J. Sorensen, and D. Henningson, 2008: Validation 562
of methods using EllipSys3D. Technical report, KTH, TRITA-MEK 2008:12. 563
Garza, J., A. Blatt, R. Gandoin, and S.-Y. Hui, 2011: Evaluation of two novel 564
wake models in offshore wind farms. Proceeding, EWEA Offshore 2011, 565
Amsterdam, The Netherlands, 29 November1 December 2011. 566
Jacobson, M. Z., and C. L. Archer, 2012: Saturation wind power potential and its 567
implications for wind energy. Proc. Natl. Acad. Sci., 109(39), 15681. 568
Jensen, N. O., 1983: A Note on Wind Generator Interaction, Risø National 569
Laboratory, Risø-M-2411 570
Jimenez, A., A. Crespo, E. Migoya, and J. Garcia, 2007: Advances in large-eddy 571
simulation of a wind turbine wake. J. Phys.: Conf. Ser., 75, 012041. 572
Jimenez, A., A. Crespo, E. Migoya, and J. Garcia, J., 2008: Large-eddy simulation 573
of spectral coherence in a wind turbine wake, Environ. Res. 3, 015004. 574
Jimenez, A., A. Crespo, and E. Migoya, 2010: Application of a LES technique to 575
characterize the wake deflection of a wind turbine in yaw, Wind Energy, 13, 576
559-572. 577
Katic, I., J. Højstrup, and N. O. Jensen, 1987: A simple model for cluster 578
efficiency, proceeding, European Wind Energy Conference 1986, Rome, 1, 579
407-410. 580
Keith, D. W., J. F. DeCarolis, D. C. Denkenberger, D. H. Lenschow, S. L. 581
Malyshev, S. Pacala, and P. J. Rasch, 2004: The influence of large-scale wind 582
power on global climate. Proc. Natl. Acad. Sci., 101(46), 16115–16120. 583
Kirk-Davidoff, D. B., and D. W. Keith, 2008: On the climate impact of surface 584
roughness anomalies. J. Atmos. Sci. 85, 2215-2234. 585
Kosovic, B., and J. D. Mirocha, 2012: “Toward High-resolution Multiscale 586
Simulations of Flows in Heterogeneous Atmospheric Boundary Layers.” 20th 587
Symposium on Boundary Layers and Turbulence, Boston, MA, 9-13 July, 588
2012. 589
Lee, S., Churchfield, M., P. Moriarty, J. Jonkman, and J. Michalakes, 2013: A 590
numerical study of atmospheric and wake turbulence impacts on wind turbine 591
fatigue loadings. J Solar Energy Engineering, 135(3), 031001. 592
Madsen, H. Aa., G. C. Larsen, T. J. Larsen, N. Troldborg, and R. Mikkelsen, 593
2010: Calibration and validation of the dynamic wake meandering model 594
implemented in the aeroelastic code HAWC2. J. Solar Energy Engineering, 595
132(4), 041014.
596
Mann, J., 1994: The spatial structure of neutral atmospheric surface-layer 597
turbulence. J. Fluid Mech., 273, 141–168. 598
Martinez, L. A., S. Leonardi, M. Churchfield, and P. Moriarty, 2012: A 599
comparison of actuator disk and actuator line wind turbine models and best 600
practices for their use, 50th AIAA Aerospace Sciences Meeting including the 601
New Horizons Forum and Aerospace Exposition, Nashville, Tennessee, 912 602
January 2012. 603
Marvel K., B. Kravitz, and K. Caldeira, 2012: Geophysical limits to global wind 604
power. Nature Climate Change, 3, 118–121. 605
Meyers J., and C. Meneveau, 2011: Optimal turbine spacing in fully developed 606
wind farm boundary layers. Wind Energy, 15, 305-317. 607
Mikkelsen, R., 2003: Actuator disk methods applied to wind turbines. Ph. D. 608
Thesis, Technical University of Denmark. 609
Mikkelsen, R., J. N. Sørensen, J. A. Michelsen, and N. Troldborg, 2005: 610
Aeroelastic actuator line modeling, proceeding, 18th IEA Symposium on 611
Aerodynamics of Wind Turbines, Pamplona, Spain, 2005. 612
National Renewable Energy Laboratory, “SOWFA: Simulator for On/Offshore 613
Wind Farm Applications,” 614
http://wind.nrel.gov/designcodes/simulators/sowfa/. 615
Rajewski, D., and coauthors, 2013: Crop Wind Energy Experiment (CWEX): 616
Observations of Surface-Layer, Boundary Layer, and Mesoscale Interactions 617
with a Wind Farm. Bull Amer Meteor Soc 94: 655–672. 618
Rhodes, M. E., and J. K. Lundquist, 2013: The Effect of Wind Turbine Wakes on 619
Summertime Midwest Atmospheric Wind Profiles. In press at Boundary-620
Layer Meteorology. doi:10.1007/s10546-013-9834-x
621
Sanderse, B., S. P. van der Pijl, and B. Koren, 2011: Review of computational 622
fluid dynamics for wind turbine wake aerodynamics. Wind Energy, 14, 799-623
819. 624
Sørensen, N. N., 2003: General Purpose Flow Solver Applied to Flow over Hills, 625
Ph.D. Thesis, Risø-R-827(EN), Risø National Laboratory, Roskilde, Denmark. 626
Sørensen, J. N., and W. Z. Shen, 2002: Numerical modeling of wind turbine 627
wakes. J. Fluids Engineering, 124(2), 393-399. 628
Thomsen, K., 2001: Aeroelastic load simulations, FLEX5 sample, Abstract from 629
C-WET expert training RISO, Risø, Denmark. 630
Troldborg, N., G. C. Larsen, H. A. Madsen, K. S. Hansen, J. N. Sørensen, and R. 631
Mikkelsen, 2010: Numerical simulations of wake interactions between two 632
wind turbines at various inflow conditions. Wind Energy, 14, 859-876. 633
Vermeer, L. J., J. N. Sørensen, and A. Crespo, A., 2003: Wind turbine wake 634
aerodynamics. Prog. Aerosp. Sci., 39, 467–510. 635
Wagner, R., M. Courtney, J. Gottschall, and P. Lindellow-Marsden, 2011; 636
Accounting for the speed shear in wind turbine power performance 637
measurement. Wind Energy, 14(8), 993-1004, doi:10.1002/we.509. 638
Wang, C., and R. J. Prinn, 2010: Potential climatic impacts and reliability of very 639
large-scale wind farms. Atmos. Chem. Phys. 10, 2053-2061. 640
Wu, Y.-T., and F. Porté-Agel, 2011: Large-eddy simulation of wind-turbine 641
wakes: evaluation of turbine parameterizations. Bound.-Lay. Meteor., 138, 642
345–366. 643
Wu, Y.-T., and F. Porté-Agel, 2013: Simulation of turbulent flow inside and 644
above wind farms: model validation and layout effects. Bound.-Lay. Meteor., 645
146, 181-205.
646
Yang, X., S. Kang, and F. Sotiropoulos F., 2012: Computational study and 647
modeling of turbine spacing effects in infinite aligned wind farms. Phys. 648
Fluids, 24, 115107.
649
Zhou, L., Y. Tian, S. Baidya Roy, C. Thorncroft, L. F. Bosart, and Y. Hu, 2012: 650
Impacts of wind farms on land surface temperature. Nature Climate Change, 2, 651
539–543, doi:10.1038. 652
